جست‌وجوی استاد و پژوهشگر

ChengXiang Zhaiمشاهده پروفایل

استاد

• Intelligent information systems
• Information retrieval
• Data mining
• +۴ مورد دیگر

ChengXiang Zhai is the Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign , holding appointments in the Department of Computer Science , Carl R. Woese Institute for Genomic Biology , and the Department of Statistics . He leads research in intelligent information systems, with a focus on information retrieval, data mining, NLP, and machine learning. His TIMAN research group and DAIS explore applications in healthcare, education, and scientific discovery. He develops MOOCs on text retrieval and mining, and has published extensively on topics including LLM alignment, user simulation, and multimodal systems. Key Research Areas : Intelligent search engines, explainable AI, human-AI collaboration, biomedical informatics Recent Trends : LLM economics, knowledge overshadowing, just-in-time recommendation systems He has received the ACM Fellow title, SIGIR Salton Award , and multiple teaching honors including Rose Award and Graduate Mentoring Award . He serves as series editor for Springer Information Retrieval Book Series .

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics
• +۶ مورد دیگر

Oliver Krauss serves as an Assistant Professor at FH Upper Austria (University of Applied Sciences Upper Austria) at the Hagenberg Campus. He is affiliated with multiple research centers including the Research Center Hagenberg, Web Intelligence and Innovation Laboratory, AIST Assistive Technology Lab, Center of Excellence Medical Technology/TIMed Center, Center of Excellence for Smart Production, and Digital Media Lab. His work falls under the Strength area ICT - Information & Communication Technology with additional connections to HEAL initiatives. Dr. Krauss's research spans several interconnected domains with a strong focus on practical applications of artificial intelligence. His primary research interests include Healthcare Informatics (particularly HL7 FHIR standards), Language Models and Natural Language Processing, Web Intelligence for user behavior analysis, Assistive Technology applications, and Process Mining for business optimization. His work demonstrates consistent interdisciplinary connections between computer science fundamentals and real-world healthcare and business applications. Analysis of Krauss's recent publications (2023-2025) reveals a clear trajectory toward increasingly sophisticated AI applications. His research has evolved from foundational IoT and sensor data processing toward advanced language model applications in healthcare informatics, e-commerce personalization, and fact-checking systems. A notable pattern is his ability to bridge theoretical AI concepts with practical implementations in medical technology and business intelligence, often incorporating emerging technologies like GPT-3 and other large language models into domain-specific solutions. Dr. Krauss has received significant recognition for his work, including: FH Upper Austria Young Researcher Award (2019 and 2022) HL7 Austria Student Award for Master's Thesis (2015) Best Paper Award at Intelligent Systems and Applications 2022 His research activities include substantial project leadership and collaboration, with notable projects including MARIE (Mobile Robotic Assistance for Office Environments), CRISAM® Loop (AI-based risk management), and EDEN (Emergency Detection for Elevator Networks). He has maintained consistent publication output with 74 publications to date and has been active in scientific communities through conference participation, invited lectures (including on HL7 FHIR topics), and committee work. His h-index of 6 reflects a solid research impact with 110 citations. Dr. Krauss leads research within multiple specialized laboratories including the Web Intelligence and Innovation Laboratory where he applies language models to website visitor segmentation, the AIST Assistive Technology Lab focusing on healthcare applications, and contributes to the Center of Excellence Medical Technology/TIMed Center where his work on patient medical records analysis has significant clinical implications.

Arjen van Dalenمشاهده پروفایل

استاد

• Political Communication
• Journalism
• Algorithmic Gatekeeping
• +۵ مورد دیگر

Arjen van Dalen is a Professor at the Department of Political Science and Public Management, Faculty of Social Sciences, University of Southern Denmark. He is affiliated with the Digital Democracy Centre and the Journalism VIP (Villum Investigator Programme). His research focuses on political communication, journalism, and quantitative/mixed methods. Education: Ph.D. in Political Science MSc in Communication Research Interests: Political communication Journalism Algorithmic gatekeeping AI in media Economic news Media trust Article Trends: His recent publications analyze algorithmic gatekeeping, AI's impact on journalism, political microtargeting, economic news consumption, and longitudinal trust in media. Key themes include automated news production, democratic implications of AI, voter behavior, and media credibility under algorithmic influence. Scientific Awards: Goldsmith Book Prize (2016) DFF-Research Project Grant (2017) Top faculty paper award from ICA Journalism Studies Section (2017) Best Book Award from The International Journal of Press/Politics (2018) Best paper in Ethics & Socially Responsible Marketing Track (2018) Teaching & Supervision: Teaches courses on Political Communication, Digital Practice, and The History, Values and Ethics of Journalism Supervised Master's Thesis by Kristiansen, M. B. Projects: Algorithmic Gatekeepers: The Democratic Consequences of News Selection Algorithms Citizens, Digitalization, and AI (CDAI) Microtargeting of Danish Voters

Xi Wangمشاهده پروفایل

مدرس

• Natural Language Processing
• Conversational AI
• Retrieval-Augmented Generation
• +۳ مورد دیگر

Dr Xi Wang is a Lecturer in the School of Computer Science at the University of Sheffield, specializing in Natural Language Processing. His research focuses on conversational AI systems, personalization techniques, retrieval-augmented generation, and fairness in AI applications. He actively contributes to the school's Natural Language Processing research group and maintains strong industry connections through publications in top-tier conferences. Dr Wang earned his PhD from the University of Glasgow under Prof. Iadh Ounis and Prof. Craig Macdonald, where he developed review text-based recommender systems. He subsequently conducted postdoctoral research at University College London's web intelligence group with Prof. Emine Yilmaz, focusing on knowledge-augmented task-oriented dialogue systems. His research spans critical NLP domains with emphasis on: Conversational AI: Building dialogue systems that handle complex user interactions Retrieval-Augmented Generation: Integrating external knowledge into language models Personalization: Developing adaptive systems that respect user context Fairness: Addressing bias in recommendation and dialogue systems Machine Learning: Applying advanced techniques to information retrieval challenges Analysis of Dr Wang's 15 most recent publications (2021-2025) reveals a clear trajectory toward more sophisticated conversational systems. His work increasingly integrates retrieval methods with generative models, particularly in recommendation contexts. Key trends include cold-start problem solutions, bias mitigation in conversational agents, and novel approaches to clarification question handling. The research demonstrates growing emphasis on fairness and knowledge grounding in dialogue systems. Dr Wang received a Google research workshop grant ($20,000) in 2022 for developing knowledge-enriched task-oriented dialogue systems during his postdoctoral work at UCL with Prof. Emine Yilmaz. While no current advisees are listed, Dr Wang's grant activity demonstrates research leadership. His Google-funded project focused on action-oriented dialogue systems, showing capability in securing competitive research funding. The Natural Language Processing research group at Sheffield provides his primary academic home, where he contributes to the school's strengths in speech and language technologies. His recent publications indicate active collaboration with researchers across multiple institutions including UCL, University of Glasgow, and international partners.

Natalie Brown Devlinمشاهده پروفایل

استادیار

• Crisis Communication
• Digital Media
• Sports Communication
• +۳ مورد دیگر

Natalie Brown Devlin is an Assistant Professor at the Stan Richards School of Advertising & Public Relations, University of Texas at Austin. Her research focuses on crisis communication and digital media within sports contexts, particularly analyzing how social media empowers organizational stakeholders during sports-related crises. She also teaches courses on digital metrics in the Texas Media Program. Devlin’s research interests include: Crisis communication strategies in sports Digital/social media engagement Fan psychology and identification Media effects on nationalism Sports-related image repair theory Algorithmic personalization in communication Her recent publications examine trends in Olympic media consumption, social media-based crisis responses, and the psychological drivers of sports fan behavior. She applies systems thinking to address fragmentation in social media research and explores intersections between political identity and sports media credibility. Devlin previously worked in digital advertising as a Senior Analyst of Strategic Account Analytics, providing data-driven insights to marketing executives.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Ernst á Heygum Kass is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. Core research areas include natural language understanding, misinformation detection, explainable AI, and multi-modal machine learning intersecting with computer vision. Applications of his affiliated section's work span automatic fact-checking, machine translation, question answering, and visually-grounded language learning.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Peter Trip Malmroes is an Instructor at the Department of Computer Science, University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. His research interests align with NLP section priorities, including applications in automatic fact-checking, machine translation, question answering, and multi-modal language processing. The section also contributes to the SCIENCE AI Centre at the university. Contact details: Email: pema@di.ku.dk Address: Universitetsparken 1, 2100 Copenhagen Ø

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۵ مورد دیگر

Daniel Nicholas Mølhave is an Instructor at the Department of Computer Science (DIKU) , University of Copenhagen, located at Universitetsparken 1, 2100 København Ø. His work is associated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. University affiliation: University of Copenhagen Department: Department of Computer Science (DIKU) Email: damo@di.ku.dk The NLP section at DIKU engages in cutting-edge research spanning core areas such as: Natural language understanding Multi-modal machine learning Explainable AI Visually-grounded language learning Cross-lingual NLP Language technologies Research applications include machine translation, misinformation detection, image captioning, and multilingual multimodal representation learning. The section contributes to the SCIENCE AI centre and offers courses in the Bachelor's and Master's programs in Computer Science, Machine Learning, and Data Science.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Benjamin Daniel Nyberg Shultz is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. His work aligns with the Natural Language Processing (NLP) section , focusing on methods for automated text processing, understanding, and generation using statistical models and machine learning. Research Focus : Core NLP tasks like parsing, cross-lingual processing, and transfer learning; applications in automatic fact-checking, machine translation, and visually grounded language learning. Teaching : Contributes to courses in Computer Science, Machine Learning, and Data Science, including advanced topics in NLP and deep learning. Collaboration : Part of the SCIENCE AI Centre, exploring intersections between NLP and computer vision in multi-modal learning.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۳ مورد دیگر

Dr. Neema Kotonya is an Honorary Research Associate in the Department of Computing at Imperial College London's Faculty of Engineering. Their research focuses on artificial intelligence, natural language processing, and responsible deployment of computational methods. Research areas: Explainable AI, Fact-Checking, Social Good Applications Key methodologies: Graph Reasoning, Policy Compliance Analysis, Model Interpretability Recent work includes frameworks for ethical NLP applications, automated verification systems, and citizen reporting datasets for civic engagement. They maintain an active publication record in computational social science and AI evaluation metrics.

Richard Fletcherمشاهده پروفایل

استاد پژوهشی

• Digital News Consumption
• Comparative Media Research
• Social Media Journalism
• +۳ مورد دیگر

Richard Fletcher is the Director of Research at the Reuters Institute for the Study of Journalism at the University of Oxford. He specializes in digital news consumption patterns, comparative media research, and the intersection of technology and journalism. His work focuses on global trends in news use, audience trust dynamics, and the role of social media in journalism. He leads the Digital News Project, co-authoring the annual Digital News Report—the world’s largest survey of global news consumption habits. Education: PhD in Sociology, University of Surrey MSc in Science, Medicine, Technology and Society, Imperial College London Undergraduate degree in Computer Science Research Interests: Algorithmic curation and platform effects Trust in news media and misinformation Generative AI's impact on journalism Cross-national comparisons of news consumption Awards: Wolfgang Donbach Award (2018) for 'Are News Audiences Increasingly Fragmented?' Nominated twice for Bob Franklin Award His research explores how audiences navigate digital news ecosystems, with recent emphasis on generative AI’s role in news production and consumption. He also investigates strategies to rebuild trust in media through audience-centric approaches.

• Computational Social Science
• Disinformation
• Extremist Groups
• +۵ مورد دیگر

Aleksi Knuutila is an anthropologist and data scientist at the Oxford Internet Institute, University of Oxford. His work bridges interpretative methods with computational analysis of digital data to study political culture, disinformation, and online communities. PhD in Digital Anthropology from University College London Collaborator in the Computational Propaganda project Focus on AI governance, hate speech detection, and political transparency His research spans disinformation , extremist groups , and social media dynamics , often integrating natural language processing and online ethnography . Projects include DemTech on algorithmic influence in public life and vaccine misinformation analysis. Knuutila's advocacy includes digital rights and FOIA-style data access in Finland via tietopyynto.fi . Collaborative outputs highlight global AI attitudes, Facebook's fact-checking gaps, and Telegram's role in far-right communication. No scientific awards are explicitly mentioned in the provided texts.

Andrew Fanoمشاهده پروفایل

استاد بالینی

• Artificial Intelligence
• Machine Learning
• Applied Artificial Intelligence
• +۷ مورد دیگر

Andrew Fano is a Clinical Professor of Computer Science at Northwestern University and serves as the McCormick Director of the Kellogg-McCormick MBAi Program. He previously spent 25 years at Accenture Labs as Global Managing Director for AI Research, leading global teams in applied AI projects across industries like healthcare, retail, and pharma. His research focuses on combining emerging AI technologies with human capabilities to solve industry-specific challenges, with notable contributions in causality detection, quantum computing, and responsible AI deployment. Fano is a top patent holder at Accenture, emphasizing practical AI applications and ethics. He also led the Accenture Labs University program, fostering academic-industry collaborations. Education: PhD in Computer Science, Northwestern University AB in Cognitive Science, Vassar College His research interests span causal reasoning in NLP, AI ethics, and cross-industry AI integration. Recent work includes analyzing diabetes-related social media for causal insights and developing quantum optimization systems. He prioritizes real-world applicability, ensuring technologies address practical business and societal needs responsibly. Awards: None explicitly listed, though recognized as a top patent holder at Accenture. Advising & Grants: Advised on multiple AI projects across industries, coordinated research sponsorships with 20+ universities through Accenture Labs. At Northwestern, leads the MBAi Program to bridge academic and corporate AI expertise. Labs/Teams: Oversees the MBAi Program’s interdisciplinary initiatives and previously managed global AI research teams at Accenture Labs, collaborating with partners in the US, Ireland, India, China, and France.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Yulong Chen is a Researcher at the Department of Computer Science and Technology, University of Cambridge. He specializes in Natural Language Processing (NLP), focusing on areas such as large language models (LLMs), constituency parsing, cross-lingual summarization, and dialogue systems. His work bridges theoretical advancements with practical applications, including automated claim verification, multimodal intelligence, and ethical AI considerations. Chen contributes to both teaching and research: he teaches courses on Natural Language Processing at both undergraduate and postgraduate levels. His research emphasizes interdisciplinary approaches, combining machine learning, computational linguistics, and cognitive models. Key projects include developing benchmarks for higher-order theory of mind reasoning in LLMs and designing frameworks to uncover model weaknesses. His publications span foundational surveys (e.g., on LLM hallucinations and text-to-SQL systems) to applied work like MACSum for controllable summarization and Hi-TOM for evaluating social cognition in AI. He collaborates extensively on tools like EASE for efficient data annotation and UniSumm for few-shot summarization, reflecting his commitment to advancing both NLP infrastructure and model capabilities.

• Natural Language Processing
• Machine Learning
• Logical Reasoning
• +۷ مورد دیگر

Zhenyun Deng is a Researcher in the Department of Computer Science and Technology at the University of Cambridge, specializing in Natural Language Processing (NLP) and Machine Learning. His work focuses on advancing automated fact-checking systems, logical reasoning in large language models, and graph-based reasoning techniques. Key research themes include document-level claim extraction, logic-driven data augmentation, and multi-hop question answering. His recent contributions span automated verification of textual claims , abstract meaning representation-based data augmentation , and robust node classification on graph data . He collaborates on initiatives like the FEVER workshop and develops datasets such as TaKG for table-to-text generation enhanced with knowledge graphs. Notable trends in his publications include: Integration of logical reasoning into NLP systems Improving model interpretability for multi-step reasoning tasks Handling noisy data in graph neural networks His research bridges foundational machine learning theory with real-world applications in information retrieval and causal inference. No awards or grants are explicitly listed in the provided materials.